Antibiotic Susceptibilities of <i>Pseudomonas aeruginosa</i> Isolates Derived from Patients with Cystic Fibrosis under Aerobic, Anaerobic, and Biofilm Conditions
Bibliographic record
Abstract
Recent studies have determined that Pseudomonas aeruginosa can live in a biofilm mode within hypoxic mucus in the airways of patients with cystic fibrosis (CF). P. aeruginosa grown under anaerobic and biofilm conditions may better approximate in vivo growth conditions in the CF airways, and combination antibiotic susceptibility testing of anaerobically and biofilm-grown isolates may be more relevant than traditional susceptibility testing under planktonic aerobic conditions. We tested 16 multidrug-resistant isolates of P. aeruginosa derived from CF patients using multiple combination bactericidal testing to compare the efficacies of double and triple antibiotic combinations against the isolates grown under traditional aerobic planktonic conditions, in planktonic anaerobic conditions, and in biofilm mode. Both anaerobically grown and biofilm-grown bacteria were significantly less susceptible (P < 0.01) to single and combination antibiotics than corresponding aerobic planktonically grown isolates. Furthermore, the antibiotic combinations that were bactericidal under anaerobic conditions were often different from those that were bactericidal against the same organisms grown as biofilms. The most effective combinations under all conditions were colistin (tested at concentrations suitable for nebulization) either alone or in combination with tobramycin (10 microg ml(-1)), followed by meropenem combined with tobramycin or ciprofloxacin. The findings of this study illustrate that antibiotic sensitivities are dependent on culture conditions and highlight the complexities of choosing appropriate combination therapy for multidrug-resistant P. aeruginosa in the CF lung.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".